Time-series data is a sequence of data points collected over regular or irregular time intervals, allowing businesses to track trends and make predictions. It plays a crucial role in various fields such as marketing, supply chain management, health care, cryptocurrency, and finance. Time-series data can be continuous, discrete, or irregular, with each type having its own unique characteristics and requiring different analytical approaches. Understanding time-series data involves examining the four components: trend, seasonality, cyclicity, and irregularity. Common tools for time-series data include data ingest tools like Apache Kafka or Prometheus, specialized databases like TimescaleDB, query tools such as SQL, and visualization tools like Matplotlib and Tableau.